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Boosted and reward-regularized classification for apprenticeship learning., , and . AAMAS, page 1249-1256. IFAAMAS/ACM, (2014)Rainbow: Combining Improvements in Deep Reinforcement Learning, , , , , , , , , and . (2017)cite arxiv:1710.02298Comment: Under review as a conference paper at AAAI 2018.The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning, , , , , and . ICLR, (2017)cite arxiv:1704.04651.Rainbow: Combining Improvements in Deep Reinforcement Learning., , , , , , , , , and . AAAI, page 3215-3222. AAAI Press, (2018)Nash Learning from Human Feedback., , , , , , , , , and 8 other author(s). ICML, OpenReview.net, (2024)Learning Nash Equilibrium for General-Sum Markov Games from Batch Data., , , and . AISTATS, volume 54 of Proceedings of Machine Learning Research, page 232-241. PMLR, (2017)End-to-end optimization of goal-driven and visually grounded dialogue systems., , , , , and . IJCAI, page 2765-2771. ijcai.org, (2017)Difference of Convex Functions Programming for Reinforcement Learning., , and . NIPS, page 2519-2527. (2014)Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning., , , , , , , , , and 4 other author(s). NeurIPS, (2020)Understanding Self-Predictive Learning for Reinforcement Learning., , , , , , , , , and 6 other author(s). ICML, volume 202 of Proceedings of Machine Learning Research, page 33632-33656. PMLR, (2023)